Are “Big Data” sucking scientific talent into big business?

Over the last few years, we’ve heard a lot about how “Big Data” are going to revolutionize science and help us create a better world. But could Big Data also harm science by luring smart young people away from the pursuit of scientific truth and toward the pursuit of profits?
My attention was drawn to this issue by a postdoc in neuroscience–I’ll call him Fred–whose research involved lots of data crunching. “I think the big science journalism story of 2014 will be the brain drain from science to industry ‘data science,'” Fred emailed me. “Up until a few years ago, at least in my field, the best grad students got jobs as professors, and the less successful grad students took jobs in industry. It is now the reverse. It’s a real trend, and it’s a big deal. One reason is that science tends not to reward the graduate students who are best at developing good software, which is exactly what science needs right now…
“Another reason, especially important for me, is the quality of research in academia and in industry. In academia, the journals tend to want the most interesting results and are not so concerned about whether the results are true. In industry data science, [your] boss just wants the truth. That’s a much more inspiring environment to work in. I like writing code and analyzing data. In industry, I can do that for most of the day. In academia, it seems like faculty have to spend most of their time writing grants and responding to emails.”
Fred sent me a link to a blog post, “The Big Data Brain Drain: Why Science is in Trouble,” that expanded on his concerns. The blogger, Jake VanderPlas, a postdoc in astrophysics at the University of Washington, claimed that Big Data is, or should be, the future of science. He wrote that “in a wide array of academic fields, the ability to effectively process data is superseding other more classical modes of research… From particle physics to genomics to biochemistry to neuroscience to oceanography to atmospheric physics and everywhere in-between, research is increasingly data-driven, and the pace of data collection shows no sign of abating.”
The problem, VanderPlas said, is that academia lags behind Big Business in recognizing the value of data-analysis talent. “The skills required to be a successful scientific researcher are increasingly indistinguishable from the skills required to be successful in industry. While academia, with typical inertia, gradually shifts to accommodate this, the rest of the world has already begun to embrace and reward these skills to a much greater degree. The unfortunate result is that some of the most promising upcoming researchers are finding no place for themselves in the academic community, while the for-profit world of industry stands by with deep pockets and open arms.”
VanderPlas and Fred, who are apparently software whizzes themselves, perhaps overstate the scientific potential of data crunching just a tad. And Fred’s aforementioned claim that industry “just wants the truth” strikes me as almost comically naïve. [*See Fred’s clarification below.] For businesses, peddling products trumps truth. But the brain drain described by Fred and VanderPlas does indeed seem significant, and disturbing.
Fred is a case in point. Increasingly despondent about his prospects in brain research, he signed up for training from Insight Data Science, which trains science Ph.D.s in data-manipulation skills that are desirable to industry (and claims to have a 100 percent job placement record). The investment paid off for Fred, who just got a job at Facebook.
*Fred explained: “When it comes to marketing a product to consumers, I agree it’s pretty obvious that business incentives are not aligned with truth telling. No one disputes that. But when it comes to the business’s internal ‘analytics’ team, the incentives are very aligned with truth telling. Analytics teams do stuff like: determining how users are interacting with the product, measuring trends in user engagement or sales, analyzing failure points in the product. This is the type of work that most data scientists do.”John Horgan directs the Center for Science Writings, which is part of the Stevens College of Arts & Letters. This column is adapted from one published on his Scientific American blog, “Cross-check.”